使用数据分解和机器学习来预测金属过程中的离子度.
Yinzhen Tan1, Wei Xu2, Kai Yang1
1State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming University of Science and Technology, Kunming, Yunnan 650093, PR China; Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650093, PR China.
The Science of the total environment
|January 14, 2025
概括
一个新的混合模型准确地预测了水金属炼中的离子度,防止了水污染,减少了资源浪费. 这种进步有助于环境管理,并优化生产成本.
科学领域:
- 环境科学 环境科学
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 固体废物对环境污染作出了重大贡献.
- 固体废物的有效管理,特别是在工业过程中,如水电金,至关重要.
- 净化废水中的离子度直接影响水污染和资源效率.
研究的目的:
- 开发一种混合预测模型,用于精确监测水金废水中的离子度.
- 解决手动测试的局限性,包括时间,成本和延误.
- 为了减轻水污染和优化粉在工业净化中的使用.
主要方法:
- 提出了一个混合模型,将数据分解和机器学习算法结合起来.
- 使用一致的训练和测试数据集进行了废弃和对比实验.
- 进行了定量指标分析,包括根平均平方误差和确定系数.
主要成果:
- 混合预测模型显示出最小的误差,最适合离子度.
- 错误指标的显著减少被观察到:RMSE (4.2%-73.9%),MAE (7.1%-93.4%),MPE (7.7%-86.7%). 在这些指标中,错误指标显著减少.
- 确定系数提高了1.3%-134.6%,表明预测准确度提高.
结论:
- 混合模型有效地预测了离子度,防止工业净化造成的水污染.
- 该模型对技术人员来说具有实际意义,可以控制粉的输入,减少资源浪费和生产成本.
- 这种方法提高了工业水电金工艺中的环境管理.
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